Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations4840
Missing cells2872
Missing cells (%)4.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory491.7 KiB
Average record size in memory104.0 B

Variable types

DateTime1
Numeric9
Categorical3

Alerts

avg_water_level is highly overall correlated with max_flow_rate and 1 other fieldsHigh correlation
max_flow_rate is highly overall correlated with avg_water_level and 1 other fieldsHigh correlation
max_water_temperature is highly overall correlated with swimming and 2 other fieldsHigh correlation
suspended_solids_concentration is highly overall correlated with avg_water_level and 1 other fieldsHigh correlation
swimming is highly overall correlated with max_water_temperature and 1 other fieldsHigh correlation
swimming_hardcore is highly overall correlated with max_water_temperature and 2 other fieldsHigh correlation
tmax is highly overall correlated with max_water_temperature and 1 other fieldsHigh correlation
wpgt is highly overall correlated with wspdHigh correlation
wspd is highly overall correlated with wpgtHigh correlation
swimming is highly imbalanced (75.1%)Imbalance
max_water_temperature has 718 (14.8%) missing valuesMissing
avg_water_level has 718 (14.8%) missing valuesMissing
suspended_solids_concentration has 718 (14.8%) missing valuesMissing
max_flow_rate has 718 (14.8%) missing valuesMissing
date has unique valuesUnique
prcp has 2494 (51.5%) zerosZeros
snow has 4495 (92.9%) zerosZeros

Reproduction

Analysis started2024-09-15 16:44:01.244561
Analysis finished2024-09-15 16:44:06.615596
Duration5.37 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

date
Date

UNIQUE 

Distinct4840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size37.9 KiB
Minimum2011-06-01 00:00:00
Maximum2024-08-31 00:00:00
2024-09-15T18:44:06.755027image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:06.860109image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

tmax
Real number (ℝ)

HIGH CORRELATION 

Distinct420
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.632066
Minimum-9.7
Maximum36.9
Zeros8
Zeros (%)0.2%
Negative149
Negative (%)3.1%
Memory size37.9 KiB
2024-09-15T18:44:06.907165image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-9.7
5-th percentile1.195
Q18.6
median15.6
Q322.9
95-th percentile29.8
Maximum36.9
Range46.6
Interquartile range (IQR)14.3

Descriptive statistics

Standard deviation9.0315209
Coefficient of variation (CV)0.57775606
Kurtosis-0.81304661
Mean15.632066
Median Absolute Deviation (MAD)7.2
Skewness-0.05128156
Sum75659.2
Variance81.56837
MonotonicityNot monotonic
2024-09-15T18:44:06.952313image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.5 31
 
0.6%
19.4 29
 
0.6%
11.9 29
 
0.6%
18.9 28
 
0.6%
15.5 25
 
0.5%
19.3 25
 
0.5%
22.2 25
 
0.5%
4.9 25
 
0.5%
22.6 24
 
0.5%
9.7 24
 
0.5%
Other values (410) 4575
94.5%
ValueCountFrequency (%)
-9.7 1
< 0.1%
-9.4 1
< 0.1%
-9.1 1
< 0.1%
-8.4 2
< 0.1%
-8.3 1
< 0.1%
-8.1 1
< 0.1%
-7.6 1
< 0.1%
-7.5 1
< 0.1%
-7.2 1
< 0.1%
-7.1 1
< 0.1%
ValueCountFrequency (%)
36.9 2
< 0.1%
36.8 1
 
< 0.1%
36.5 1
 
< 0.1%
36.2 1
 
< 0.1%
35.9 2
< 0.1%
35.8 2
< 0.1%
35.6 2
< 0.1%
35.5 3
0.1%
35.4 1
 
< 0.1%
35.3 2
< 0.1%

prcp
Real number (ℝ)

ZEROS 

Distinct281
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6331364
Minimum0
Maximum91.5
Zeros2494
Zeros (%)51.5%
Negative0
Negative (%)0.0%
Memory size37.9 KiB
2024-09-15T18:44:07.010408image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.5
95-th percentile13.905
Maximum91.5
Range91.5
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation5.9474209
Coefficient of variation (CV)2.2586832
Kurtosis32.635783
Mean2.6331364
Median Absolute Deviation (MAD)0
Skewness4.540171
Sum12744.38
Variance35.371815
MonotonicityNot monotonic
2024-09-15T18:44:07.061571image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2494
51.5%
0.1 144
 
3.0%
0.2 114
 
2.4%
0.3 84
 
1.7%
0.4 54
 
1.1%
0.5 53
 
1.1%
0.6 52
 
1.1%
0.7 52
 
1.1%
0.9 50
 
1.0%
1.3 48
 
1.0%
Other values (271) 1695
35.0%
ValueCountFrequency (%)
0 2494
51.5%
0.1 144
 
3.0%
0.2 114
 
2.4%
0.3 84
 
1.7%
0.4 54
 
1.1%
0.5 53
 
1.1%
0.6 52
 
1.1%
0.7 52
 
1.1%
0.8 43
 
0.9%
0.9 50
 
1.0%
ValueCountFrequency (%)
91.5 1
< 0.1%
71.2 1
< 0.1%
66.9 1
< 0.1%
61.7 1
< 0.1%
56.9 1
< 0.1%
56.7 1
< 0.1%
54.5 1
< 0.1%
49.2 1
< 0.1%
46.4 1
< 0.1%
46.3 1
< 0.1%

snow
Real number (ℝ)

ZEROS 

Distinct30
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9504132
Minimum0
Maximum420
Zeros4495
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size37.9 KiB
2024-09-15T18:44:07.114473image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile30
Maximum420
Range420
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.274503
Coefficient of variation (CV)4.9035308
Kurtosis77.167591
Mean4.9504132
Median Absolute Deviation (MAD)0
Skewness7.5168594
Sum23960
Variance589.25152
MonotonicityNot monotonic
2024-09-15T18:44:07.155163image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 4495
92.9%
40 43
 
0.9%
10 42
 
0.9%
20 36
 
0.7%
30 36
 
0.7%
50 29
 
0.6%
70 22
 
0.5%
80 20
 
0.4%
60 17
 
0.4%
90 16
 
0.3%
Other values (20) 84
 
1.7%
ValueCountFrequency (%)
0 4495
92.9%
10 42
 
0.9%
20 36
 
0.7%
30 36
 
0.7%
40 43
 
0.9%
50 29
 
0.6%
60 17
 
0.4%
70 22
 
0.5%
80 20
 
0.4%
90 16
 
0.3%
ValueCountFrequency (%)
420 1
< 0.1%
410 1
< 0.1%
370 1
< 0.1%
310 1
< 0.1%
300 1
< 0.1%
250 2
< 0.1%
230 1
< 0.1%
220 1
< 0.1%
210 1
< 0.1%
200 2
< 0.1%

wspd
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9577293
Minimum0.7
Maximum36.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.9 KiB
2024-09-15T18:44:07.202799image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile5.4
Q17.2
median9
Q311.5
95-th percentile18.4
Maximum36.7
Range36
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation4.2033278
Coefficient of variation (CV)0.4221171
Kurtosis4.5150085
Mean9.9577293
Median Absolute Deviation (MAD)2.2
Skewness1.7867879
Sum48195.41
Variance17.667965
MonotonicityNot monotonic
2024-09-15T18:44:07.265142image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.6 258
 
5.3%
8.3 255
 
5.3%
6.8 252
 
5.2%
7.2 242
 
5.0%
9 222
 
4.6%
8.6 221
 
4.6%
7.9 220
 
4.5%
6.5 214
 
4.4%
6.1 208
 
4.3%
9.7 200
 
4.1%
Other values (81) 2548
52.6%
ValueCountFrequency (%)
0.7 1
 
< 0.1%
1.1 2
 
< 0.1%
1.4 1
 
< 0.1%
1.8 1
 
< 0.1%
2.5 1
 
< 0.1%
3.2 2
 
< 0.1%
3.6 3
 
0.1%
4 10
 
0.2%
4.3 37
0.8%
4.7 49
1.0%
ValueCountFrequency (%)
36.7 1
< 0.1%
35.3 1
< 0.1%
34.6 1
< 0.1%
32.4 1
< 0.1%
32 1
< 0.1%
31.3 2
< 0.1%
31 1
< 0.1%
30.6 2
< 0.1%
29.9 1
< 0.1%
29.5 2
< 0.1%

wpgt
Real number (ℝ)

HIGH CORRELATION 

Distinct234
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.946771
Minimum9.4
Maximum119.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.9 KiB
2024-09-15T18:44:07.318299image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum9.4
5-th percentile17.3
Q124.1
median31.7
Q342.1
95-th percentile64.115
Maximum119.9
Range110.5
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.695314
Coefficient of variation (CV)0.42050564
Kurtosis1.6831382
Mean34.946771
Median Absolute Deviation (MAD)8.6
Skewness1.1930944
Sum169142.37
Variance215.95226
MonotonicityNot monotonic
2024-09-15T18:44:07.368924image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.8 75
 
1.5%
21.6 73
 
1.5%
24.1 70
 
1.4%
24.5 68
 
1.4%
31.7 68
 
1.4%
26.6 67
 
1.4%
25.2 67
 
1.4%
23.4 65
 
1.3%
28.4 65
 
1.3%
20.9 64
 
1.3%
Other values (224) 4158
85.9%
ValueCountFrequency (%)
9.4 1
 
< 0.1%
10.1 1
 
< 0.1%
10.8 4
 
0.1%
11.5 3
 
0.1%
11.9 4
 
0.1%
12.2 5
0.1%
12.6 5
0.1%
13 6
0.1%
13.3 11
0.2%
13.7 11
0.2%
ValueCountFrequency (%)
119.9 1
< 0.1%
118.8 1
< 0.1%
100.4 1
< 0.1%
100.1 1
< 0.1%
98.6 1
< 0.1%
97.2 1
< 0.1%
96.1 1
< 0.1%
95.8 1
< 0.1%
95 1
< 0.1%
94 1
< 0.1%

max_water_temperature
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct212
Distinct (%)5.1%
Missing718
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean11.254755
Minimum0.5
Maximum21.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.9 KiB
2024-09-15T18:44:07.434348image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile4.2
Q16.5
median11
Q315.8
95-th percentile19.4
Maximum21.9
Range21.4
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation5.1044763
Coefficient of variation (CV)0.45353953
Kurtosis-1.2229101
Mean11.254755
Median Absolute Deviation (MAD)4.6
Skewness0.14120817
Sum46392.1
Variance26.055679
MonotonicityNot monotonic
2024-09-15T18:44:07.493299image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8 51
 
1.1%
6.2 46
 
1.0%
5.2 46
 
1.0%
5.8 45
 
0.9%
5.1 42
 
0.9%
14 41
 
0.8%
5.4 41
 
0.8%
6 40
 
0.8%
9.8 40
 
0.8%
7.5 39
 
0.8%
Other values (202) 3691
76.3%
(Missing) 718
 
14.8%
ValueCountFrequency (%)
0.5 3
0.1%
0.6 1
 
< 0.1%
0.7 1
 
< 0.1%
1 1
 
< 0.1%
1.1 2
< 0.1%
1.2 3
0.1%
1.3 3
0.1%
1.4 2
< 0.1%
1.6 3
0.1%
1.7 1
 
< 0.1%
ValueCountFrequency (%)
21.9 1
 
< 0.1%
21.8 2
 
< 0.1%
21.7 3
0.1%
21.6 3
0.1%
21.5 5
0.1%
21.4 4
0.1%
21.3 5
0.1%
21.2 4
0.1%
21.1 3
0.1%
21 5
0.1%

avg_water_level
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct220
Distinct (%)5.3%
Missing718
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean91.763464
Minimum27
Maximum339
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.9 KiB
2024-09-15T18:44:07.554490image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile48
Q167
median87
Q3108
95-th percentile159
Maximum339
Range312
Interquartile range (IQR)41

Descriptive statistics

Standard deviation36.047982
Coefficient of variation (CV)0.39283589
Kurtosis4.8875305
Mean91.763464
Median Absolute Deviation (MAD)20
Skewness1.5739583
Sum378249
Variance1299.457
MonotonicityNot monotonic
2024-09-15T18:44:07.611786image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 74
 
1.5%
94 65
 
1.3%
89 63
 
1.3%
83 63
 
1.3%
92 63
 
1.3%
77 62
 
1.3%
85 61
 
1.3%
91 59
 
1.2%
93 58
 
1.2%
58 58
 
1.2%
Other values (210) 3496
72.2%
(Missing) 718
 
14.8%
ValueCountFrequency (%)
27 9
0.2%
28 2
 
< 0.1%
29 5
 
0.1%
30 3
 
0.1%
31 2
 
< 0.1%
32 4
 
0.1%
33 5
 
0.1%
34 14
0.3%
35 12
0.2%
36 5
 
0.1%
ValueCountFrequency (%)
339 1
< 0.1%
330 1
< 0.1%
324 1
< 0.1%
312 1
< 0.1%
307 1
< 0.1%
303 1
< 0.1%
298 1
< 0.1%
293 1
< 0.1%
287 1
< 0.1%
276 1
< 0.1%

suspended_solids_concentration
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2021
Distinct (%)49.0%
Missing718
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean28.684668
Minimum1.39
Maximum2000.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.9 KiB
2024-09-15T18:44:07.670391image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1.39
5-th percentile2.2805
Q15.1825
median8.805
Q314.57
95-th percentile67.8025
Maximum2000.97
Range1999.58
Interquartile range (IQR)9.3875

Descriptive statistics

Standard deviation128.84849
Coefficient of variation (CV)4.4918941
Kurtosis177.73031
Mean28.684668
Median Absolute Deviation (MAD)4.21
Skewness12.438859
Sum118238.2
Variance16601.933
MonotonicityNot monotonic
2024-09-15T18:44:07.889596image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.95 17
 
0.4%
6 14
 
0.3%
52.71 13
 
0.3%
2.26 10
 
0.2%
2.07 10
 
0.2%
4.88 10
 
0.2%
2.99 9
 
0.2%
7.2 9
 
0.2%
6.07 9
 
0.2%
9.15 9
 
0.2%
Other values (2011) 4012
82.9%
(Missing) 718
 
14.8%
ValueCountFrequency (%)
1.39 1
 
< 0.1%
1.45 1
 
< 0.1%
1.59 1
 
< 0.1%
1.6 1
 
< 0.1%
1.63 3
0.1%
1.64 1
 
< 0.1%
1.65 1
 
< 0.1%
1.66 2
< 0.1%
1.67 3
0.1%
1.7 1
 
< 0.1%
ValueCountFrequency (%)
2000.97 1
< 0.1%
2000.86 1
< 0.1%
2000.84 1
< 0.1%
2000.78 1
< 0.1%
2000.71 2
< 0.1%
2000.53 1
< 0.1%
2000.51 1
< 0.1%
2000.49 1
< 0.1%
2000.45 1
< 0.1%
2000.42 1
< 0.1%

max_flow_rate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct991
Distinct (%)24.0%
Missing718
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean72.761815
Minimum14.8
Maximum603
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.9 KiB
2024-09-15T18:44:07.961047image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum14.8
5-th percentile23.5
Q135.4
median56.2
Q389.475
95-th percentile181
Maximum603
Range588.2
Interquartile range (IQR)54.075

Descriptive statistics

Standard deviation57.466374
Coefficient of variation (CV)0.78978754
Kurtosis12.992216
Mean72.761815
Median Absolute Deviation (MAD)24.5
Skewness2.879601
Sum299924.2
Variance3302.3842
MonotonicityNot monotonic
2024-09-15T18:44:08.010812image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 30
 
0.6%
103 23
 
0.5%
106 20
 
0.4%
107 18
 
0.4%
105 18
 
0.4%
26.5 17
 
0.4%
27.2 17
 
0.4%
24.3 16
 
0.3%
111 16
 
0.3%
29.7 16
 
0.3%
Other values (981) 3931
81.2%
(Missing) 718
 
14.8%
ValueCountFrequency (%)
14.8 1
< 0.1%
15.1 2
< 0.1%
15.2 2
< 0.1%
15.3 1
< 0.1%
15.4 1
< 0.1%
15.8 1
< 0.1%
15.9 1
< 0.1%
16.7 2
< 0.1%
16.9 1
< 0.1%
17 1
< 0.1%
ValueCountFrequency (%)
603 1
< 0.1%
571 1
< 0.1%
547 1
< 0.1%
523 1
< 0.1%
522 1
< 0.1%
461 1
< 0.1%
459 2
< 0.1%
430 1
< 0.1%
429 1
< 0.1%
425 1
< 0.1%

swimming
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.9 KiB
0
4639 
1
 
201

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4840
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4639
95.8%
1 201
 
4.2%

Length

2024-09-15T18:44:08.060907image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T18:44:08.103523image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4639
95.8%
1 201
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 4639
95.8%
1 201
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4639
95.8%
1 201
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4639
95.8%
1 201
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4639
95.8%
1 201
 
4.2%

swimming_hardcore
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.9 KiB
0
4203 
1
637 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4840
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 4203
86.8%
1 637
 
13.2%

Length

2024-09-15T18:44:08.133799image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T18:44:08.177796image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4203
86.8%
1 637
 
13.2%

Most occurring characters

ValueCountFrequency (%)
0 4203
86.8%
1 637
 
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4203
86.8%
1 637
 
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4203
86.8%
1 637
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4203
86.8%
1 637
 
13.2%

ice_bath
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.9 KiB
0
4087 
1
753 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4840
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4087
84.4%
1 753
 
15.6%

Length

2024-09-15T18:44:08.208629image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T18:44:08.248978image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4087
84.4%
1 753
 
15.6%

Most occurring characters

ValueCountFrequency (%)
0 4087
84.4%
1 753
 
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4087
84.4%
1 753
 
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4087
84.4%
1 753
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4087
84.4%
1 753
 
15.6%

Interactions

2024-09-15T18:44:05.865029image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:01.675737image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.324993image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.880532image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.246920image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.852121image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:04.238260image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:04.757670image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:05.322366image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:05.908043image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:01.887770image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.372617image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.918561image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.299149image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.895664image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:04.282935image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:04.818068image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:05.365979image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:05.952511image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:01.957529image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.417034image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.961407image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.340161image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.937009image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:04.343464image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:04.966686image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:05.414639image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:06.109170image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.004208image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.457653image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.999625image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.382744image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.977694image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:04.384520image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:05.025383image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:05.473921image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:06.154456image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.063953image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.500461image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.041723image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.429816image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:04.019718image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:04.431629image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:05.075177image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:05.524158image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:06.202410image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.113449image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.561088image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.082646image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.558577image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:04.062159image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:04.471370image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:05.120793image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:05.569089image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:06.245511image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.162768image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.619689image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.122470image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.605038image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:04.106717image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:04.512338image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:05.164124image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:05.711758image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:06.291055image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.222956image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.791771image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.161521image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.648546image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:04.147442image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:04.555565image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:05.213932image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:05.760953image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:06.338441image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.274583image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:02.838310image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.205848image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:03.695003image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:04.196716image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:04.601196image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:05.275494image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-15T18:44:05.816736image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2024-09-15T18:44:08.282273image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
avg_water_levelice_bathmax_flow_ratemax_water_temperatureprcpsnowsuspended_solids_concentrationswimmingswimming_hardcoretmaxwpgtwspd
avg_water_level1.0000.3590.8090.2860.087-0.1960.5080.1350.1740.2800.0860.042
ice_bath0.3591.0000.3460.4730.0980.0760.0680.0870.1660.3510.2950.285
max_flow_rate0.8090.3461.0000.2280.132-0.1570.6550.1600.1810.2350.1050.095
max_water_temperature0.2860.4730.2281.0000.019-0.4080.2610.5770.6830.8790.034-0.118
prcp0.0870.0980.1320.0191.0000.0320.0890.0330.016-0.0730.4250.312
snow-0.1960.076-0.157-0.4080.0321.000-0.1290.0100.067-0.406-0.0400.042
suspended_solids_concentration0.5080.0680.6550.2610.089-0.1291.0000.0000.0590.2600.1070.081
swimming0.1350.0870.1600.5770.0330.0100.0001.0000.5330.4810.0950.090
swimming_hardcore0.1740.1660.1810.6830.0160.0670.0590.5331.0000.5670.1690.156
tmax0.2800.3510.2350.879-0.073-0.4060.2600.4810.5671.0000.070-0.113
wpgt0.0860.2950.1050.0340.425-0.0400.1070.0950.1690.0701.0000.848
wspd0.0420.2850.095-0.1180.3120.0420.0810.0900.156-0.1130.8481.000

Missing values

2024-09-15T18:44:06.411993image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-15T18:44:06.502657image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-15T18:44:06.573564image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

datetmaxprcpsnowwspdwpgtmax_water_temperatureavg_water_levelsuspended_solids_concentrationmax_flow_rateswimmingswimming_hardcoreice_bath
02011-06-0113.34.20.014.044.616.1107.0126.14192.0000
12011-06-0217.10.00.010.832.813.282.0114.6998.7000
22011-06-0323.80.00.015.143.614.862.090.8960.0000
32011-06-0427.80.00.012.231.316.452.067.0942.4010
42011-06-0527.14.70.09.767.717.149.043.2943.6000
52011-06-0624.613.10.08.655.117.163.021.5970.4000
62011-06-0725.20.00.09.034.916.756.016.2645.5010
72011-06-0819.31.90.012.651.516.552.014.4042.3000
82011-06-0915.40.40.07.619.814.952.012.5545.2000
92011-06-1018.60.00.06.821.213.861.011.2059.6000
datetmaxprcpsnowwspdwpgtmax_water_temperatureavg_water_levelsuspended_solids_concentrationmax_flow_rateswimmingswimming_hardcoreice_bath
48302024-08-2224.40.00.05.419.4018.4131.034.9575.2010
48312024-08-2329.50.00.07.629.2019.1127.034.9566.2010
48322024-08-2431.40.00.07.627.0020.0126.034.9567.7010
48332024-08-2523.66.70.09.442.1019.7121.034.9587.5010
48342024-08-2616.30.60.05.418.0017.4142.034.9596.9000
48352024-08-2724.60.00.08.631.7017.6130.034.9576.7010
48362024-08-2829.60.00.05.034.0919.0117.034.9553.0010
48372024-08-2931.20.00.06.123.8019.6114.034.9546.1010
48382024-08-3030.80.00.06.124.5019.8114.034.9546.1010
48392024-08-3130.00.00.08.333.8019.7109.034.9544.8010